Overview

Dataset statistics

Number of variables19
Number of observations9578
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

NUM11
BOOL8

Reproduction

Analysis started2022-01-15 14:42:59.340579
Analysis finished2022-01-15 14:43:32.844494
Duration33.5 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

revol.bal has 321 (3.4%) zeros Zeros
revol.util has 297 (3.1%) zeros Zeros
inq.last.6mths has 3637 (38.0%) zeros Zeros
delinq.2yrs has 8458 (88.3%) zeros Zeros
pub.rec has 9019 (94.2%) zeros Zeros

Variables

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.8 KiB
1
7710
0
1868
ValueCountFrequency (%) 
1771080.5%
 
0186819.5%
 

int.rate
Real number (ℝ≥0)

Distinct count249
Unique (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12264006055543955
Minimum0.06
Maximum0.2164
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2022-01-15T14:43:32.918859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.0774
Q10.1039
median0.1221
Q30.1407
95-th percentile0.167
Maximum0.2164
Range0.1564
Interquartile range (IQR)0.0368

Descriptive statistics

Standard deviation0.02684698721
Coefficient of variation (CV)0.2189087896
Kurtosis-0.2243235112
Mean0.1226400606
Median Absolute Deviation (MAD)0.0186
Skewness0.1644199135
Sum1174.6465
Variance0.0007207607224
2022-01-15T14:43:33.067317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.12533543.7%
 
0.08942993.1%
 
0.11832432.5%
 
0.12182152.2%
 
0.09632102.2%
 
0.11142062.2%
 
0.081982.1%
 
0.12871972.1%
 
0.11481932.0%
 
0.09321872.0%
 
Other values (239)727676.0%
 
ValueCountFrequency (%) 
0.0680.1%
 
0.06394< 0.1%
 
0.067690.1%
 
0.0705230.2%
 
0.071290.1%
 
ValueCountFrequency (%) 
0.21642< 0.1%
 
0.212170.1%
 
0.2092< 0.1%
 
0.208660.1%
 
0.20524< 0.1%
 

installment
Real number (ℝ≥0)

Distinct count4788
Unique (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.08941323867197
Minimum15.67
Maximum940.14
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2022-01-15T14:43:33.254275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile65.5595
Q1163.77
median268.95
Q3432.7625
95-th percentile756.2655
Maximum940.14
Range924.47
Interquartile range (IQR)268.9925

Descriptive statistics

Standard deviation207.0713015
Coefficient of variation (CV)0.6489444429
Kurtosis0.1379077383
Mean319.0894132
Median Absolute Deviation (MAD)124.7
Skewness0.9125224624
Sum3056238.4
Variance42878.5239
2022-01-15T14:43:33.358310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
317.72410.4%
 
316.11340.4%
 
319.47290.3%
 
381.26270.3%
 
662.68270.3%
 
156.1240.3%
 
320.95240.3%
 
669.33230.2%
 
334.67230.2%
 
188.02230.2%
 
Other values (4778)930397.1%
 
ValueCountFrequency (%) 
15.671< 0.1%
 
15.691< 0.1%
 
15.751< 0.1%
 
15.761< 0.1%
 
15.911< 0.1%
 
ValueCountFrequency (%) 
940.141< 0.1%
 
926.832< 0.1%
 
922.421< 0.1%
 
918.022< 0.1%
 
916.952< 0.1%
 

log.annual.inc
Real number (ℝ≥0)

Distinct count1987
Unique (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.93211713780027
Minimum7.547501682999999
Maximum14.52835448
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2022-01-15T14:43:33.531237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7.547501683
5-th percentile9.917893268
Q110.55841352
median10.92888357
Q311.29129292
95-th percentile11.91839057
Maximum14.52835448
Range6.980852797
Interquartile range (IQR)0.7328793975

Descriptive statistics

Standard deviation0.6148127514
Coefficient of variation (CV)0.05623912949
Kurtosis1.609004138
Mean10.93211714
Median Absolute Deviation (MAD)0.366945765
Skewness0.02866810657
Sum104707.8179
Variance0.3779947192
2022-01-15T14:43:33.702107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11.002099843083.2%
 
10.819778282482.6%
 
10.596634732242.3%
 
10.308952662242.3%
 
10.714417772212.3%
 
11.225243391962.0%
 
11.156250521651.7%
 
10.778956291491.6%
 
10.915088461471.5%
 
11.082142551461.5%
 
Other values (1977)755078.8%
 
ValueCountFrequency (%) 
7.5475016831< 0.1%
 
7.600902461< 0.1%
 
8.1016777471< 0.1%
 
8.1605182471< 0.1%
 
8.1886891241< 0.1%
 
ValueCountFrequency (%) 
14.528354481< 0.1%
 
14.180153671< 0.1%
 
14.124464771< 0.1%
 
13.997832111< 0.1%
 
13.710150042< 0.1%
 

dti
Real number (ℝ≥0)

Distinct count2529
Unique (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.60667884735853
Minimum0.0
Maximum29.96
Zeros89
Zeros (%)0.9%
Memory size74.8 KiB
2022-01-15T14:43:33.868695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.27
Q17.2125
median12.665
Q317.95
95-th percentile23.65
Maximum29.96
Range29.96
Interquartile range (IQR)10.7375

Descriptive statistics

Standard deviation6.883969541
Coefficient of variation (CV)0.5460573418
Kurtosis-0.9003553617
Mean12.60667885
Median Absolute Deviation (MAD)5.385
Skewness0.02394102295
Sum120746.77
Variance47.38903664
2022-01-15T14:43:34.023301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0890.9%
 
10190.2%
 
0.6160.2%
 
13.16130.1%
 
19.2130.1%
 
15.1130.1%
 
12130.1%
 
6130.1%
 
13.28120.1%
 
10.8120.1%
 
Other values (2519)936597.8%
 
ValueCountFrequency (%) 
0890.9%
 
0.011< 0.1%
 
0.021< 0.1%
 
0.031< 0.1%
 
0.042< 0.1%
 
ValueCountFrequency (%) 
29.961< 0.1%
 
29.951< 0.1%
 
29.91< 0.1%
 
29.741< 0.1%
 
29.721< 0.1%
 

fico
Real number (ℝ≥0)

Distinct count44
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.8463144706619
Minimum612
Maximum827
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2022-01-15T14:43:34.169626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum612
5-th percentile657
Q1682
median707
Q3737
95-th percentile782
Maximum827
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation37.97053723
Coefficient of variation (CV)0.05341595849
Kurtosis-0.4223123103
Mean710.8463145
Median Absolute Deviation (MAD)25
Skewness0.4712597399
Sum6808486
Variance1441.761697
2022-01-15T14:43:34.316970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6875485.7%
 
6825365.6%
 
6924985.2%
 
6974765.0%
 
7024724.9%
 
7074444.6%
 
6674384.6%
 
6774274.5%
 
7174244.4%
 
6624144.3%
 
Other values (34)490151.2%
 
ValueCountFrequency (%) 
6122< 0.1%
 
6171< 0.1%
 
6221< 0.1%
 
6272< 0.1%
 
63260.1%
 
ValueCountFrequency (%) 
8271< 0.1%
 
82250.1%
 
81760.1%
 
812330.3%
 
807450.5%
 

days.with.cr.line
Real number (ℝ≥0)

Distinct count2687
Unique (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4560.767196529213
Minimum178.95833330000002
Maximum17639.95833
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2022-01-15T14:43:34.460214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum178.9583333
5-th percentile1320.041667
Q12820
median4139.958333
Q35730
95-th percentile9329.958333
Maximum17639.95833
Range17461
Interquartile range (IQR)2910

Descriptive statistics

Standard deviation2496.930377
Coefficient of variation (CV)0.5474803403
Kurtosis1.937860594
Mean4560.767197
Median Absolute Deviation (MAD)1440.083334
Skewness1.155748227
Sum43683028.21
Variance6234661.307
2022-01-15T14:43:34.633598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3660500.5%
 
3630480.5%
 
3990460.5%
 
4410440.5%
 
3600410.4%
 
2550380.4%
 
4080380.4%
 
3690370.4%
 
1800370.4%
 
4020350.4%
 
Other values (2677)916495.7%
 
ValueCountFrequency (%) 
178.95833331< 0.1%
 
180.04166673< 0.1%
 
1811< 0.1%
 
183.04166671< 0.1%
 
209.04166671< 0.1%
 
ValueCountFrequency (%) 
17639.958331< 0.1%
 
176161< 0.1%
 
166521< 0.1%
 
163501< 0.1%
 
162601< 0.1%
 

revol.bal
Real number (ℝ≥0)

ZEROS

Distinct count7869
Unique (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16913.963875548132
Minimum0
Maximum1207359
Zeros321
Zeros (%)3.4%
Memory size74.8 KiB
2022-01-15T14:43:34.808200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile127.7
Q13187
median8596
Q318249.5
95-th percentile57654.3
Maximum1207359
Range1207359
Interquartile range (IQR)15062.5

Descriptive statistics

Standard deviation33756.18956
Coefficient of variation (CV)1.995758641
Kurtosis259.655203
Mean16913.96388
Median Absolute Deviation (MAD)6488
Skewness11.16105849
Sum162001946
Variance1139480333
2022-01-15T14:43:35.015532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03213.4%
 
255100.1%
 
298100.1%
 
68290.1%
 
34680.1%
 
18260.1%
 
108560.1%
 
222960.1%
 
803550.1%
 
650.1%
 
Other values (7859)919296.0%
 
ValueCountFrequency (%) 
03213.4%
 
150.1%
 
22< 0.1%
 
31< 0.1%
 
42< 0.1%
 
ValueCountFrequency (%) 
12073591< 0.1%
 
9520131< 0.1%
 
6025191< 0.1%
 
5089611< 0.1%
 
4077941< 0.1%
 

revol.util
Real number (ℝ≥0)

ZEROS

Distinct count1035
Unique (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.79923574859052
Minimum0.0
Maximum119.0
Zeros297
Zeros (%)3.1%
Memory size74.8 KiB
2022-01-15T14:43:35.162831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q122.6
median46.3
Q370.9
95-th percentile94
Maximum119
Range119
Interquartile range (IQR)48.3

Descriptive statistics

Standard deviation29.01441697
Coefficient of variation (CV)0.6199762988
Kurtosis-1.116466996
Mean46.79923575
Median Absolute Deviation (MAD)24.2
Skewness0.05998544258
Sum448243.08
Variance841.8363919
2022-01-15T14:43:35.326658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02973.1%
 
0.5260.3%
 
0.3220.2%
 
73.7220.2%
 
47.8220.2%
 
3.3210.2%
 
0.1210.2%
 
0.2200.2%
 
0.7200.2%
 
1200.2%
 
Other values (1025)908794.9%
 
ValueCountFrequency (%) 
02973.1%
 
0.041< 0.1%
 
0.1210.2%
 
0.2200.2%
 
0.3220.2%
 
ValueCountFrequency (%) 
1191< 0.1%
 
108.81< 0.1%
 
106.51< 0.1%
 
106.41< 0.1%
 
106.21< 0.1%
 

inq.last.6mths
Real number (ℝ≥0)

ZEROS

Distinct count28
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5774692002505741
Minimum0
Maximum33
Zeros3637
Zeros (%)38.0%
Memory size74.8 KiB
2022-01-15T14:43:35.471539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.200245315
Coefficient of variation (CV)1.394794469
Kurtosis26.28813144
Mean1.5774692
Median Absolute Deviation (MAD)1
Skewness3.584150856
Sum15109
Variance4.841079446
2022-01-15T14:43:35.639400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0363738.0%
 
1246225.7%
 
2138414.4%
 
38649.0%
 
44755.0%
 
52782.9%
 
61651.7%
 
71001.0%
 
8720.8%
 
9470.5%
 
Other values (18)941.0%
 
ValueCountFrequency (%) 
0363738.0%
 
1246225.7%
 
2138414.4%
 
38649.0%
 
44755.0%
 
ValueCountFrequency (%) 
331< 0.1%
 
321< 0.1%
 
311< 0.1%
 
281< 0.1%
 
271< 0.1%
 

delinq.2yrs
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1637084986427229
Minimum0
Maximum13
Zeros8458
Zeros (%)88.3%
Memory size74.8 KiB
2022-01-15T14:43:35.808582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5462149246
Coefficient of variation (CV)3.336509278
Kurtosis71.43318185
Mean0.1637084986
Median Absolute Deviation (MAD)0
Skewness6.061793276
Sum1568
Variance0.2983507439
2022-01-15T14:43:35.942714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0845888.3%
 
18328.7%
 
21922.0%
 
3650.7%
 
4190.2%
 
560.1%
 
62< 0.1%
 
71< 0.1%
 
131< 0.1%
 
111< 0.1%
 
ValueCountFrequency (%) 
0845888.3%
 
18328.7%
 
21922.0%
 
3650.7%
 
4190.2%
 
ValueCountFrequency (%) 
131< 0.1%
 
111< 0.1%
 
81< 0.1%
 
71< 0.1%
 
62< 0.1%
 

pub.rec
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06212152850281896
Minimum0
Maximum5
Zeros9019
Zeros (%)94.2%
Memory size74.8 KiB
2022-01-15T14:43:36.115057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2621263264
Coefficient of variation (CV)4.219573032
Kurtosis38.7810072
Mean0.0621215285
Median Absolute Deviation (MAD)0
Skewness5.12643446
Sum595
Variance0.06871021098
2022-01-15T14:43:36.261306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0901994.2%
 
15335.6%
 
2190.2%
 
350.1%
 
51< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
0901994.2%
 
15335.6%
 
2190.2%
 
350.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
51< 0.1%
 
41< 0.1%
 
350.1%
 
2190.2%
 
15335.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.8 KiB
0
8045
1
 
1533
ValueCountFrequency (%) 
0804584.0%
 
1153316.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
8316
1
 
1262
ValueCountFrequency (%) 
0831686.8%
 
1126213.2%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
5621
1
3957
ValueCountFrequency (%) 
0562158.7%
 
1395741.3%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
9235
1
 
343
ValueCountFrequency (%) 
0923596.4%
 
13433.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
8949
1
 
629
ValueCountFrequency (%) 
0894993.4%
 
16296.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
9141
1
 
437
ValueCountFrequency (%) 
0914195.4%
 
14374.6%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
8959
1
 
619
ValueCountFrequency (%) 
0895993.5%
 
16196.5%
 

Interactions

2022-01-15T14:43:10.978209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:11.217431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:11.377402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:11.556202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:11.724496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:11.884842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:12.062334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:12.244023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:12.392175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:12.569992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:12.746057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:12.910013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:13.059561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:13.210550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:13.392164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:13.577780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:13.761800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:13.950989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:14.084332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:14.262697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:14.420400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:14.562751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:14.738842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:14.894786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:15.060983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:15.233010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:15.382505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:15.551141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:15.712763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:16.090430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:16.281435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:16.483026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:16.674147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:16.845710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:16.989075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:17.164782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:17.335898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:17.479928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:17.652798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:17.815239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:17.975496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:18.141716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:18.277398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:18.455123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:18.572449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:18.762030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:18.914308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.062181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.229656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.361452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.538861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.678206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.844030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:19.972471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:20.149407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:20.267070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:20.488523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:20.663368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:20.841322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:20.988301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:21.164792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:21.340335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:21.491444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:21.666742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:21.837653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:21.989130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:22.185304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:22.389711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:22.555489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:22.734628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:23.133646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:23.260587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:23.461667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:23.642753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:23.789641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:23.965306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:24.141932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:24.285472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:24.475602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:24.636897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:24.784227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:24.962034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:25.124389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:25.274057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:25.453278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:25.613836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:25.782239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:25.946194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:26.087060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:26.297426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:26.467574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:26.634561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:26.784522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:26.958673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:27.173486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:27.345948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:27.502682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:27.672523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:27.841026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:27.983232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:28.175810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:28.339538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:28.481140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:28.669409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:28.836197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:29.009808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:29.193332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:29.381044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:29.578882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:29.758789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:29.922101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:30.077947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:30.229939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:30.376788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:30.544146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:30.707994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:30.867594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:31.038216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:31.177637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:31.356199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:31.803759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-15T14:43:36.463917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-15T14:43:36.747527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-15T14:43:37.039031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-15T14:43:37.339474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-15T14:43:32.121670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-15T14:43:32.618603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

credit.policyint.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.recnot.fully.paidpurpose_credit_cardpurpose_debt_consolidationpurpose_educationalpurpose_home_improvementpurpose_major_purchasepurpose_small_business
010.1189829.1011.35040719.487375639.9583332885452.10000010000
110.1071228.2211.08214314.297072760.0000003362376.70000100000
210.1357366.8610.37349111.636824710.000000351125.61000010000
310.1008162.3411.3504078.107122699.9583333366773.21000010000
410.1426102.9211.29973214.976674066.000000474039.50100100000
510.0788125.1311.90496816.987276120.0416675080751.00000100000
610.1496194.0210.7144184.006673180.041667383976.80011010000
710.1114131.2211.00210011.087225116.0000002422068.60001000000
810.113487.1911.40756517.256823989.0000006990951.11000000100
910.122184.1210.20359210.007072730.041667563023.01000010000

Last rows

credit.policyint.rateinstallmentlog.annual.incdtificodays.with.cr.linerevol.balrevol.utilinq.last.6mthsdelinq.2yrspub.recnot.fully.paidpurpose_credit_cardpurpose_debt_consolidationpurpose_educationalpurpose_home_improvementpurpose_major_purchasepurpose_small_business
956800.197937.0610.64542522.176675916.0000002885459.86010000000
956900.1426823.3412.4292163.627223239.9583333357583.95001000100
957000.1671113.6310.64542528.066723210.0416672575963.85001000000
957100.1568161.0111.2252438.006777230.000000690929.24011000000
957200.156569.9810.1104727.026628190.041667299939.56001010000
957300.1461344.7612.18075510.3967210474.00000021537282.12001000000
957400.1253257.7011.1418620.217224380.0000001841.15001000000
957500.107197.8110.59663513.096873450.0416671003682.98001010000
957600.1600351.5810.81977819.186921800.00000003.25001000100
957700.1392853.4311.26446416.287324740.0000003787957.06001010000